Natural variations in underwater noise levels in the \nEastern Grand Banks, Newfoundland
Bibliographic record
Abstract
“The topic of underwater noise pollution due to oil & gas exploration is a genuine con- \ncern to scientists and researchers. Studying acoustic propagation from noise sources has \nbecome one of the standard environmental impact assessment criteria for offshore devel- \nopments. Lower level noise is also a concern when persistent and higher than naturally \noccurring background noise. The natural environment contributes sound through wind \nand wave motion, currents, precipitation, and sea ice. A two-month autonomous acous- \ntic monitoring program was conducted in 2015 on the Grand Banks, as part of a study to \nunderstand the impact of seismic surveys off the coast of Newfoundland. This study aims \nto use that data to improve our understanding of the ambient soundscape and relate the \nobservations to known relationships between noise levels and wind and rainfall rates. A \nchallenge with the present data is that the observations are made in shallow water where \nbottom and surface reflections act to increase expected natural sound levels. This increase \nin sound levels interferes with algorithms used to relate wind and rain to noise levels. An \nanalytical model was used to adjust noise levels accounting for the shallow water envi- \nronment. The corrected data were evaluated using algorithms for Weather classification \ndeveloped by Nystuen which were used to identify the data points with sound levels asso- \nciated with shipping, drizzle, rain and near surface bubbles. The shipping contamination \nwas removed from the data sets and resulting sorted data was used to estimate wind speeds \nthat were compared to independent observations obtained from model data provided by \nDepartment of Fisheries and Oceans, Canada.”
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".